How to make sure AI and ML models survive and thrive
SAS' Véronique Van Vlasselaer reveals why managing model performance is as important as putting them into production.
Tag: best practices
Maximize model performance without maximizing effort
Through hyperparameter autotuning, you can maximize a model's performance without maximizing effort. While SAS searches the hyperparameter space in the background, you are free to pursue other work.
Model selection for spatial econometrics using PROC SPATIALREG
A previous post, Spatial econometric modeling using PROC SPATIALREG, introduced the SAS/ETS® SPATIALREG procedure and demonstrated its usage to fit both linear and SAR models by using 2013 county-level home value data in North Carolina. In most analysis for spatial econometrics, you rarely know the true model from which your data
Local Search Optimization for HyperParameter Tuning
When shopping for a new TV, with many sets next to each other across a store wall, it is easy to compare the picture quality and brightness. What is not immediately evident and expected is the difference between how the set looked in the store and how it looks in your
Optimization for machine learning and monster trucks
Optimization for machine learning is essential to ensure that data mining models can learn from training data in order to generalize to future test data. Data mining models can have millions of parameters that depend on the training data and, in general, have no analytic definition. In such cases, effective models
Understanding data mining clustering methods
When you go to the grocery store, you see that items of a similar nature are displayed nearby to each other. When you organize the clothes in your closet, you put similar items together (e.g. shirts in one section, pants in another). Every personal organizing tip on the web to
I've seen the future of data science....
"I've seen the future of data science, and it is filled with estrogen!" This was the opening remark at a recent talk I heard. If only I'd seen that vision of the future when I was in college. You see, I’ve always loved math (and still do). My first calculus
Self-service analytics with SAS – and what we should NOT borrow from the ancient Egyptians
I recently read the book "Die Zahl die aus der Kälte kam" (which would be The Number That Came in from the Cold in English) written by the Austrian mathematician Rudolf Taschner. He is ingenious at presenting complex mathematical relationships to a broader audience. One of his examples deals with
Of Big Data Competitions, Sports Analytics, and the Internet of Bugs
It is said that everything is big in Texas, and that includes big data. During my recent trip to Austin I had the privilege of being a judge in the final round of the Texata Big Data World Championship, a fantastic example of big data competitions. It felt fitting that
Vector autoregressive models for generating economic scenarios
Macroeconometrics is not dead: (and I wish I had paid better attention in my time series course): I wrote this on the way to see one of our manufacturing clients in Austin, Texas, anticipating a discussion how to use vector autoregressive models in process control. It is a typical use